class_docstr: Uses different parameters by forecasting horizon element.
  
  When provided with forecasting horizon [f1, f2, ..., fn],
  will fit forecaster with fh=f1 and parameters fh_params[f1] to forecast f1,
  forecaster with fh=f2 and parameters fh_params[f2] to forecast f2, etc.
  
  To use different estimators per horizon, combine ``FhPlexForecaster`` with
  one of ``MultiplexForecaster`` and ``MultiplexTransformer``.
  
  Parameters
  ----------
  forecaster : sktime compatible forecaster
  fh_params : dict, list, callable, or str that eval-defines a callable
      specifies forecaster to use per fh element
      dict: keys = fh elements, values = param dict for forecaster
      list: i-th entry is forecaster param dict for i-th fh element
      callable: maps fh element to forecaster param dict
      str: eval(fh_params) must define a lambda that maps fh element to param dict
      param dict need not be complete, only overrides for ``forecaster`` params
  fh_lookup : str, one of "relative" (default), "absolute", or "as-is"
      specifies fh elements used in dict or callable
      if "relative", fh will be coerced to relative ForecastingHorizon
      if "absolute", fh will be coerced to absolute ForecastingHorizon
      if "as-is", fh will be coerced to ForecastingHorizon (but not relative/absolute)
  fh_contiguous : bool, default=False
      whether fh in inner loops are enforced to be contiguous
      False: forecaster with fh_params[fN] is asked to forecast fN and only fN
      True: forecaster with fh_params[fN] is asked to forecast 1, 2, ..., fN
          and the output is then subset to the forecast of fN
          this is required if the forecaster can only forecast contiguous horizons
      CAUTION: if using grid search inside, then ``True`` will cause the
      tuning metric to be evaluated on horizons 1, 2, ..., fN, not just fN
  
  Attributes
  ----------
  forecasters_ : dict of sktime forecaster
      keys are fh elements (coerced according to ``fh_lookup``)
      entries are clones of ``forecaster`` used for fitting and forecasting
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.forecasting.compose import FhPlexForecaster
  
  Simple example - same parameters per fh element
  >>> y = load_airline()
  >>> f = FhPlexForecaster(NaiveForecaster())
  >>> f.fit(y, fh=[1, 2, 3])
  FhPlexForecaster(...)
  >>> f.forecasters_  # get individual fitted forecasters
  {1: NaiveForecaster(), 2: NaiveForecaster(), 3: NaiveForecaster()}
  >>> fitted_params = f.get_fitted_params()  # or via get_fitted_params
  >>> y_pred = f.predict()
  
  Simple example - different parameters per fh element
  >>> y = load_airline()
  >>> fh_params = [{}, {"strategy": "last"}, {"strategy": "mean"}]
  >>> f = FhPlexForecaster(NaiveForecaster(), fh_params=fh_params)
  >>> f.fit(y, fh=[1, 2, 3])
  FhPlexForecaster(...)
  >>> f.forecasters_  # get individual fitted forecasters
  {1: NaiveForecaster(), 2: NaiveForecaster(), 3: NaiveForecaster(strategy='mean')}
  >>> y_pred = f.predict()
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docstr: Compute/return prediction quantiles for a forecast.
  
  private _predict_quantiles containing the core logic,
      called from predict_quantiles and possibly predict_interval
  
  State required:
      Requires state to be "fitted".
  
  Accesses in self:
      Fitted model attributes ending in "_"
      self.cutoff
  
  Parameters
  ----------
  fh : guaranteed to be ForecastingHorizon
      The forecasting horizon with the steps ahead to to predict.
  X : optional (default=None)
      guaranteed to be of a type in self.get_tag("X_inner_mtype")
      Exogeneous time series to predict from.
  alpha : list of float (guaranteed not None and floats in [0,1] interval)
      A list of probabilities at which quantile forecasts are computed.
  
  Returns
  -------
  pred_quantiles : pd.DataFrame
      Column has multi-index: first level is variable name from y in fit,
          second level being the quantile forecasts for each alpha.
          Quantile forecasts are calculated for each a in alpha.
      Row index is fh. Entries are quantile forecasts, for var in col index,
          at quantile probability in second-level col index, for each row index.
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code: def _predict_quantiles(self, fh, X, alpha):
      """Compute/return prediction quantiles for a forecast.
  
      private _predict_quantiles containing the core logic,
          called from predict_quantiles and possibly predict_interval
  
      State required:
          Requires state to be "fitted".
  
      Accesses in self:
          Fitted model attributes ending in "_"
          self.cutoff
  
      Parameters
      ----------
      fh : guaranteed to be ForecastingHorizon
          The forecasting horizon with the steps ahead to to predict.
      X : optional (default=None)
          guaranteed to be of a type in self.get_tag("X_inner_mtype")
          Exogeneous time series to predict from.
      alpha : list of float (guaranteed not None and floats in [0,1] interval)
          A list of probabilities at which quantile forecasts are computed.
  
      Returns
      -------
      pred_quantiles : pd.DataFrame
          Column has multi-index: first level is variable name from y in fit,
              second level being the quantile forecasts for each alpha.
              Quantile forecasts are calculated for each a in alpha.
          Row index is fh. Entries are quantile forecasts, for var in col index,
              at quantile probability in second-level col index, for each row index.
      """
      y_pred = self._get_preds(
          fh, "predict_quantiles", X=X, alpha=alpha
      )
      return y_pred
  
file_path: sktime/sktime/forecasting/compose/_fhplex.py
imports: from sktime.forecasting.base import BaseForecaster
  import pandas
  from sktime.datatypes._utilities import get_slice
label_desc: Use image node_id 10 for calling the FhPlexForecaster obj's underlying member method code with example usage: obj._predict_quantiles(fh, X, alpha) and returns: y_pred